Evidence map›Paper›PMID 40215974›Full record

ReviewCell genomics2025

Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.

Yu-Ning Huang, Viorel Munteanu, Michael I Love, Cynthia Flaire Ronkowski, Dhrithi Deshpande, Annie Wong-Beringer, Russell Corbett-Detig, Mihai Dimian, Jason H Moore, Lana X Garmire and 6 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. UShER-TB: Scalable, Comprehensive, Accessible Phylogenomic Analysis ofmedRxiv : the preprint server for health sciences · 2025
    Article
  12. Review
  13. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Yu-Ning HuangDepartment of Clinical Pharmacy, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089, USA.
Viorel MunteanuDepartment of Computers, Informatics, and Microelectronics, Technical University of Moldova, 2045 Chisinau, Moldova; Department of Biological and Morphofunctional Sciences, College of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, 720229 Suceava, Romania.
Michael I LoveDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
Cynthia Flaire RonkowskiDepartment of Clinical Pharmacy, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089, USA.
Dhrithi DeshpandeDepartment of Clinical Pharmacy, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089, USA.
Annie Wong-BeringerDepartment of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
Russell Corbett-DetigGenomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
Mihai DimianDepartment of Computers, Electronics, and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania.
Jason H MooreDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90069, USA.
Lana X GarmireDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48105, USA.
T B K ReddyUS Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Atul J ButteBakar Computational Health Sciences Institute, University of California, San Francisco (UCSF), San Francisco, CA 94143, USA; Center for Data-Driven Insights and Innovation, University of California, Oakland, Oakland, CA 94607, USA.
Mark D RobinsonSIB Swiss Institute of Bioinformatics and Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.
Eleazar EskinDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
Malak S AbedalthagafiDepartment of Pathology and Laboratory Medicine, Emory University Hospital, Atlanta, GA, USA.
Serghei MangulDepartment of Clinical Pharmacy, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Computers, Informatics, and Microelectronics, Technical University of Moldova, 2045 Chisinau, Moldova; Department of Biological and Morphofunctional Sciences, College of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; Sage Bionetworks, Seattle, WA, USA. Electronic address: serghei.mangul@gmail.com.

Funding

Advancing method benchmarking and data sharing through crowd-sourced competitions in cancer researchU24CA248265 · NCI · SAGE BIONETWORKS · PI BOUTROS, PAUL CHRISTOPHER, VARMA, SUSHEEL · 2020 to 2024
$4.1M
NCI NIH HHS U24 CA248265
6 · The paper itself

Abstract

Metadata, or "data about data," is essential for organizing, understanding, and managing large-scale omics datasets. It enhances data discovery, integration, and interpretation, enabling reproducibility, reusability, and secondary analysis. However, metadata sharing remains hindered by perceptual and technical barriers, including the lack of uniform standards, privacy concerns, study design limitations, insufficient incentives, inadequate infrastructure, and a shortage of trained personnel. These challenges compromise data reliability and obstruct integrative meta-analyses. Addressing these issues requires standardization, education, stronger roles for journals and funding agencies, and improved incentives and infrastructure. Looking ahead, emerging technologies such as artificial intelligence and machine learning may offer promising solutions to automate metadata processes, increasing accuracy and scalability. Fostering a collaborative culture of metadata sharing will maximize the value of omics data, accelerating innovation and scientific discovery.

Indexed as

GenomicsInformation DisseminationMetadataArtificial IntelligenceHumansMachine Learningbarriers in metadata sharing practicesdatametadatametadata completeness

Identifiers

PMID40215974
PMCPMC12143318

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.